Designing Committees for Mitigating Biases

Authors: Michal Feldman, Yishay Mansour, Noam Nisan, Sigal Oren, Moshe Tennenholtz1942-1949

AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical We study a novel model of voting in which a committee of experts is constructed to reduce the biases of its members. We first present voting rules that optimally reduce the biases of a given committee. Our main results include the design of committees, for several settings, that are able to reach a nearly optimal (unbiased) choice. We also provide a thorough analysis of the trade-offs between the committee size and the obtained error.
Researcher Affiliation Collaboration 1Tel-Aviv University, Israel, 2Microsoft Research, Israel, 3Google Research, Israel, 4Hebrew University, Israel 5Ben-Gurion University of the Negev, Israel, 6Technion Israel Institute of Technology, Israel
Pseudocode No The paper describes computational procedures and reductions (e.g., building a directed graph and solving shortest paths in Theorem 3.2) in paragraph form, but it does not present them as structured pseudocode or algorithm blocks.
Open Source Code No The paper does not provide any statement or link regarding the release of source code for the described methodology.
Open Datasets No The paper is theoretical and does not mention the use of any datasets for training or evaluation.
Dataset Splits No The paper is theoretical and does not describe data partitioning into training, validation, or test sets.
Hardware Specification No The paper is theoretical and does not describe the hardware used for any experiments.
Software Dependencies No The paper is theoretical and does not mention specific software dependencies with version numbers.
Experiment Setup No The paper is theoretical and does not describe experimental setup details such as hyperparameters or training configurations.